import tensorflow as tf
from keras import datasets

import os

# 0,1,2,3分别是对应log的四个等级，
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

# 首先要加载数据集
# x:[60k,28,28]
# y:[60k]
(x, y), _ = datasets.mnist.load_data()
# x[0-255]-->[0-1.]
x = tf.convert_to_tensor(x, dtype=tf.float32) / 255.
y = tf.convert_to_tensor(y, dtype=tf.int32)


print(x.shape, y.shape, x.dtype, y.dtype)
print(tf.reduce_min(x), tf.reduce_max(x))
print(tf.reduce_min(y), tf.reduce_max(y))


# 每次取128个数据
train_db = tf.data.Dataset.from_tensor_slices((x, y)).batch(128)
train_iter = iter(train_db)
sample = next(train_iter)
print('batch', sample[0].shape, sample[1].shape)

# [b,784]-->[b,256]--->[b,128]--->[b,10]
w1 = tf.Variable(tf.random.truncated_normal([784, 256], stddev=0.1))
b1 = tf.Variable(tf.zeros([256]))
w2 = tf.Variable(tf.random.truncated_normal([256, 128], stddev=0.1))
b2 = tf.Variable(tf.zeros([128]))
w3 = tf.Variable(tf.random.truncated_normal([128, 10], stddev=0.1))
b3 = tf.Variable(tf.zeros([10]))

# h1-->h2--->out

lr = 1e-3

for epoch in range(10):  # iter for train_db
    for step, (x, y) in enumerate(train_db):  # for every batch
        # x:[128,28,28]
        # y:[128]
        # [b,28,28]--->[b,28*28]
        x = tf.reshape(x, [-1, 28 * 28])

        with tf.GradientTape() as tape:  # tf.Variable

            # [b,784]@[784,256]+[256]---->[b,256]+[256]
            # 这里的broadcast_to是可以省略的，因为他会自动计算
            h1 = x @ w1 + tf.broadcast_to(b1, [x.shape[0], 256])
            h1 = tf.nn.relu(h1)
            h2 = h1 @ w2 + b2
            h2 = tf.nn.relu(h2)
            out = h2 @ w3 + b3

            # 计算loss
            # out:[b,10]
            # y:[b]---->[b,10]
            y_onehot = tf.one_hot(y, depth=10)

            # mes=mean(sum((y-out)^2))
            loss = tf.square(y_onehot - out)
            # meac: saclar
            loss = tf.reduce_mean(loss)

            # compute gradients
            grads = tape.gradient(loss, [w1, b1, w2, b2, w3, b3])
            # 原地更新
            w1.assign_sub(lr * grads[0])
            b1.assign_sub(lr * grads[1])
            w2.assign_sub(lr * grads[2])
            b2.assign_sub(lr * grads[3])
            w3.assign_sub(lr * grads[4])
            b3.assign_sub(lr * grads[5])

            if step % 100 == 0:
                print(epoch, step, 'loss', float(loss))
